A comprehensive 25-phase, 139-week software engineering mastery roadmap — Version 9.0 (2026 Enhanced Edition) — from fundamentals to industry-ready expertise.
This repository contains a structured, project-based software engineering learning roadmap (V9.0) designed to take you from the absolute basics all the way to professional mastery. Spanning 25 phases, 163 topics, and 139 weeks, it provides a clear, week-by-week curriculum covering every major area of modern software engineering — including systems programming, algorithms, full-stack web development, AI/ML/LLMs, DevOps, cloud architecture, and career preparation. Updated for 2025–2026 with the latest technology landscape.
The roadmap is aimed at self-taught developers who want a structured path without a formal degree, CS students looking to complement their coursework with practical industry skills, and career switchers who need a comprehensive guide to break into software engineering. Each phase builds on the previous one, ensuring a solid foundation before advancing to more complex topics.
The learning philosophy is project-based and consistent: dedicate approximately 4 hours per day to hands-on coding and conceptual study. Rather than passive reading, every phase emphasizes building real projects to reinforce concepts. The roadmap progresses through four tiers — Foundations, Core Engineering, Specialization, and Mastery — each tier unlocking the next level of expertise.
V9.0 is a major update reflecting the 2025–2026 technology landscape:
- TikZ visual roadmap — full-page tier-progression flowchart showing all 25 phases across 4 tiers
- Phase 17 (Gen AI & LLMs) massively expanded — AI agents, MCP, RLVR, DeepSeek R1, updated model references (Claude 4, GPT-5, Gemini 2.5)
- 6 new topics added (163 total) — AI-Assisted Dev Workflows, Vector Databases, Modern Frameworks (htmx/Bun/Next.js 15), State-Space Models & MoE, AI Agents & Agentic Workflows, LLMOps
- Python 3.13/3.14 — free-threaded mode, JIT compiler, template strings
- Java 24/25 — virtual threads at scale, structured concurrency
- TypeScript 7.0 — Go-based compiler (10× faster), Rust 2025 edition, Bun runtime
- Kubernetes v1.35, OpenTofu, edge K8s — CI/CD pipeline diagram
- AI security — prompt injection, model poisoning, passkeys/WebAuthn
- Apache Airflow 3.x, DuckDB, Apache Iceberg — modern data stack
- vLLM, TGI, LLMOps — high-throughput LLM serving
- 2026 hiring landscape — AI-era career strategies
- 12 new glossary terms, 30+ new resources, updated appendices
The roadmap is organized into 4 tiers containing 25 phases across 139 weeks:
| Tier | Phases | Weeks | Focus Areas |
|---|---|---|---|
| Tier 1 — Foundations | 1–5 | 1–26 | C Programming, Python (Fundamentals & Advanced), Java, Git & Collaboration |
| Tier 2 — Core Engineering | 6–11 | 27–65 | Data Structures & Algorithms, Databases & SQL, Networking & Linux, Web & API Engineering, Testing & CI/CD |
| Tier 3 — Specialization | 12–23 | 66–116 | Math for AI, Machine Learning, Deep Learning, DevOps & K8s, Architecture, Gen AI & LLMs, Data Engineering, System Design, Security, Observability, MLOps & LLMOps, Go/Rust/TypeScript |
| Tier 4 — Mastery | 24–25 | 117–139 | Capstone Projects, Career & Interview Preparation |
📋 Full Phase Breakdown
| Phase | Weeks | Topic |
|---|---|---|
| Phase 1 | 1–5 | Computational Thinking & C Programming |
| Phase 2 | 6–11 | Python — Fundamentals Through Intermediate |
| Phase 3 | 12–17 | Python — Advanced & Scientific Stack |
| Phase 4 | 18–24 | Java — Fundamentals Through Advanced |
| Phase 5 | 25–28 | Git, Code Review & Collaboration |
| Phase 6 | 29–34 | DSA Part 1 — Linear Structures & Trees |
| Phase 7 | 35–40 | DSA Part 2 — Graphs, DP & Advanced Patterns |
| Phase 8 | 41–46 | Databases & SQL Mastery |
| Phase 9 | 47–51 | Networking & Linux Administration |
| Phase 10 | 52–58 | Full-Stack Web & API Engineering |
| Phase 11 | 59–63 | Testing, CI/CD & Quality Engineering |
| Phase 12 | 64–68 | Mathematics for AI |
| Phase 13 | 69–74 | Machine Learning |
| Phase 14 | 75–81 | Deep Learning |
| Phase 15 | 82–87 | DevOps, Docker & Kubernetes |
| Phase 16 | 88–92 | Design Patterns & Architecture |
| Phase 17 | 93–98 | Generative AI, LLMs & Prompt Engineering |
| Phase 18 | 99–103 | Data Engineering |
| Phase 19 | 104–108 | System Design |
| Phase 20 | 109–113 | Security Engineering |
| Phase 21 | 114–117 | Observability & Monitoring |
| Phase 22 | 118–122 | MLOps & LLMOps |
| Phase 23 | 123–128 | Modern Languages — Go, Rust & TypeScript |
| Phase 24 | 129–134 | Capstone Projects |
| Phase 25 | 135–139 | Career, Interview Preparation & Beyond |
The roadmap PDF is automatically compiled and deployed to GitHub Pages on every update.
👉 Download / View the PDF Roadmap
git clone https://github.com/gkrishna247/software-engineering-roadmap.git
cd software-engineering-roadmap
pdflatex roadmap.texSee the How to Compile Locally section for prerequisites.
| File | Description |
|---|---|
roadmap.tex |
The full roadmap source written in LaTeX |
roadmap.pdf |
Pre-compiled PDF (auto-generated by GitHub Actions) |
Resources/ |
Curated collection of technical books and external learning materials |
prompt-collection.md |
5 optimized Perplexity AI Deep Research prompts for enhancing the roadmap |
.github/workflows/compile-latex.yml |
GitHub Actions workflow that auto-compiles LaTeX to PDF and deploys to GitHub Pages |
LICENSE |
MIT License |
Install a LaTeX distribution on your system:
- Linux: TeX Live
sudo apt-get install texlive-full
- macOS: MacTeX
brew install --cask mactex
- Windows: MiKTeX
# Navigate to the repository directory
cd software-engineering-roadmap
# Compile the LaTeX source to PDF
pdflatex roadmap.tex
# Run twice to resolve cross-references (optional but recommended)
pdflatex roadmap.texThe compiled roadmap.pdf will appear in the same directory.
Every time a change is pushed to the repository that modifies roadmap.tex, a GitHub Actions workflow (.github/workflows/compile-latex.yml) automatically:
- Compiles
roadmap.textoroadmap.pdfusing TeX Live - Commits the updated PDF back to the repository
- Deploys the latest PDF to GitHub Pages for instant online access
This means the online PDF is always up to date with the latest version of the roadmap source.
The prompt-collection.md file contains 5 carefully crafted prompts designed for use with Perplexity AI Deep Research. These prompts help you:
- Validate and update topic coverage against current industry standards
- Generate detailed week-by-week project ideas for specific phases
- Research the best learning resources for each technology stack
- Identify skill gaps and suggest supplementary content
- Benchmark the roadmap against real-world job requirements
How to use:
- Open Perplexity AI and enable Deep Research mode
- Copy a prompt from
prompt-collection.md - Paste it into Perplexity AI and review the research results
- Use the insights to tailor your study plan or suggest improvements via a GitHub Issue
Contributions are welcome! Whether you've spotted an error, want to suggest a new topic, or have a resource recommendation:
- Open an Issue — describe the improvement or correction you'd like to see
- Fork & PR — fork the repository, make your changes to
roadmap.tex, and open a pull request - Discuss — share ideas or feedback in GitHub Discussions (if enabled)
Please keep contributions focused, well-reasoned, and aligned with the roadmap's project-based learning philosophy.
This project is licensed under the MIT License — see the LICENSE file for details.
Copyright © 2026 KRISHNAMOORTHI G
⭐ If this roadmap helps you, please consider giving it a star!